Recently, there has been much interest in using reinforcement learning to learn locomotion policies for legged robots [1]}, [2]}, [3]}.
However, the resulting policy is usually significantly slower and less robust compared to model-based controllers. Extra effort is also usually required to transfer the learned policy to the real robot, and researchers have proposed a variety of strategies including accurate dynamics modeling [1]}, [2]}, imitating animals [6]} and meta learning [7]}, [8]}.
To approximate the gait periodicity, [9]}, [10]} introduced sinusoidal priors into the policy design, but do not observe clear gait transition in the rollout.
In contrast to this previous work, our learned policy walks significantly faster (up to 2.5m/s, or 5.5 body length/s), transfers directly to the real robot, and shows clear gait transitions during robot operation.
